The Motivation of Capital-giving in Crowdfunding Market: A Self-determination Theory Perspective

How to promote crowd-funding results successfully are crucial to crowdfunding platforms and crowdfunding projects. The results of crowd-funding projects are determined by investors’ subjective behavior, which is triggered by some certain motivations. However, for different investors, the motivation toward a speculative behavior may be different. Thus, it is very necessary to explore and analyze the composition of the motivations behind each investor’s decision. In this paper, we identify different motivation modes mainly influenced by the project description, which will be beneficial to identify the investment intention of each investor. Based on the self-determination theory, we first create the corpus targeting different motives by means of the text mining method. Then, we classify the project description and project investment options. Last, we conduct an econometric model to examine the effect of investor’s motives on crowd-funding results based on the real dataset from Indiegogo Platform

Thermal storage/management system with phase change materials for building

Peer reviewe

Thermal Performance Analysis of an Underground Closed Chamber with Human Body Heat Sources under Natural Convection

In this article, a combined experimental and numerical study has been performed to investigate the thermal performance of a mine refuge chamber (MRC) under natural convection. In the current study, a 20-hour heating experiment is carried out in a fifty-person MRC laboratory and the heat lamps are utilized to simulate the human heat loss. A new analytical model is proposed to predict the air temperature and validated against the experimental data. Sensitivity analysis is performed to further investigate the effects of the thermal parameters of the rock. Results indicated that: (1) two different air temperature increase stages, rapid and slow increase stages, are observed in the MRC; (2) A new analytical method for predicting the air temperature in MRC under natural convection is proposed, it shows that the air temperature increasing trend becomes slow with the increase of the thermal conductivity, density and specific heat capacity of the rock; (3) the surface heat transfer coefficient on the vertical walls reaches the largest and it increases linearly with air temperature.Peer reviewe

Optimization design of annular axial cooling fan based on circumferential vorticity analysis

As complex axial flow machinery, lots of vortex distributes around engine cooling fan. Negative circumferential vorticity (CV) leads to low efficiency and power loss. In order to investigate the adverse effects of CV on aerodynamic performances of the fan, a mathematical physical relationship between CV and aerodynamic performances is established, and the location of the negative CV is found by the method of vorticity analysis. An outer ring is designed for annular cooling fan, and the parameter of aperture rate is defined in this paper. Both static pressure and power loss is overall considerate during the optimization process of the outer ring, and putting forward that optimum range of the aperture rate is different for various annular fans

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches

A Model-Driven Method for Quality Reviews Detection: An Ensemble Model of Feature Selection

With the rapid growth of e-commerce and user-generated content online, the increasing product online reviews have significant influence on both buyers and sellers. However, among the thousands of online reviews, only the reviews of high-quality matters to the market, thus quality reviews detection rises in response to the requirement of retrieving authentic feedbacks from consumers. In this paper, a state-of-the-art ensemble model, gradient boosting decision trees (GBDT), is applied to select useful features for quality evaluation of online reviews. Firstly, four types of features are extracted based on information adoption theory. Then, the GBDT model is adopted to select useful features for quality reviews detection. At last, comparative experiments are conducted through online reviews of searching goods, based on two baseline models such as Decision Tree and Logistic Regression, and the results show that GBDT model achieves a better performance in detecting reviews of high-quality. This research indicates that product attributes, reviewer characteristics and objectiveness of reviews are key ingredients in high quality reviews

Constraints on cosmic star formation history via a new modeling of the radio luminosity function of star-forming galaxies

Radio wavelengths offer a unique possibility to trace the total star-formation rate (SFR) in galaxies, both obscured and unobscured. To probe the dust-unbiased star-formation history, an accurate measurement of the radio luminosity function (LF) for star-forming galaxies (SFGs) is crucial. We make use of an SFG sample (5900 sources) from the Very Large Array (VLA) COSMOS 3 GHz data to perform a new modeling of the radio LF. By integrating the analytical LF, we aim to calculate the history of the cosmic SFR density (SFRD) from $z\sim5$ onwards. For the first time, we use both models of the pure luminosity evolution (PLE) and joint luminosity+density evolution (LADE) to fit the LFs directly to the radio data using a full maximum-likelihood analysis, considering the sample completeness correction. We also incorporate updated observations of local radio LFs and radio source counts into the fitting process to obtain additional constraints. We find that the PLE model cannot be used to describe the evolution of the radio LF at high redshift ($z>2$). By construct, our LADE models can successfully fit a large amount of data on radio LFs and source counts of SFGs from recent observations. We therefore conclude that density evolution is genuinely indispensable in modeling the evolution of SFG radio LFs. Our SFRD curve shows a good fit to the SFRD points derived by previous radio estimates. In view of the fact that our radio LFs are not biased, as opposed those of previous studies performed by fitting the $1/V_{\rm max}$ LF points, our SFRD results should be an improvement on these previous estimates. Below $z\sim1.5$, our SFRD matches a published multiwavelength compilation, while our SFRD turns over at a slightly higher redshift ($2<z<2.5$) and falls more rapidly out to high redshift.Comment: 14 pages, 10 figures, final version, to be published in A&